Anthropic has poached such an array of high-profile professors that it has become a punch line in academia. “‘I’m joining Anthropic’ is the new meme right now,” Subbarao Kambhampati, a computer-science professor at Arizona State University (who has not joined Anthropic), told us. This month, the AI company hired the chair of UC Berkeley’s department of electrical engineering and computer science, presumably to help build more capable bots. Perhaps more surprisingly, Anthropic has in recent weeks also picked up a Stanford economist, a theoretical physicist from the University of Maryland, and an analytic philosopher from UT Austin.
AI companies are turning into something like mini-universities in their own right. OpenAI employs top mathematicians and physicists—including one who studies black holes, and another who specializes in string theory. At least three computer-science professors joined Meta’s AI lab in late June. DeepMind, like Anthropic, is home to a crew of philosophers. And Anthropic’s recent job postings indicate an interest in hiring legal scholars and political scientists. It’s unclear exactly how many current and former professors are working at AI companies. Across these four firms, we found more than 80—the majority of whom are computer scientists. Some have left academia entirely; others are still working part-time at a university. That number is likely a significant undercount, because we don’t have access to internal data; it also doesn’t include the many professors who have started their own companies, those at other AI start-ups, and those working with the industry in a less formal capacity.
Particularly for AI researchers, these companies have a strong gravitational pull. “Much of the important research is being done in industry now,” Humphrey Shi, a computer-science professor at Georgia Tech who joined Nvidia as a vice president last fall, told us. As he sees it, “If you want to do something that really, truly matters, you probably want to join one of those entities.” Tech firms are making offers—including very enticing salaries—that are hard for academics to refuse. In the process, research that previously would have happened in the open is getting locked up behind closed doors.
For decades, universities were the center of AI research. The field itself officially began at a gathering of researchers at Dartmouth in 1956, and federal funding provided much of the field’s early support. In the early 2010s, Silicon Valley executives began to take AI’s commercial potential more seriously, and set out to hire the best researchers. In 2013, Google paid $44 million to acquire a start-up run by a trio of AI researchers from the University of Toronto. Facebook then hired Yann LeCun, an NYU professor, to establish the company’s AI-research lab; Uber poached some 40 Carnegie Mellon researchers to work on driverless cars. But for the most part, even as more work was being done inside of private companies, many of the newly hired academics at tech companies retained their professorships and established a culture of open research. “Researchers will be strongly encouraged to publish their work,” OpenAI wrote in its founding announcement. (Note the organization’s name.) This norm helped lead to the current AI boom: In 2017, scientists at Google published a research paper that was immediately of interest to OpenAI. Google’s innovation, called the “transformer,” is what the T in ChatGPT stands for.
As AI has taken center stage, Silicon Valley has intensified its efforts to recruit star researchers—and looked beyond computer-science departments. Philosophers help train tech companies’ bots to better interact with humans, and economists study the labor-market implications of AI. Compensation is only part of the draw. The current era of AI research requires massive amounts of computing power. Universities have only a fraction of the resources that Silicon Valley can provide, and the chasm has widened as the Trump administration has cut back on scientific funding. Anca Dragan, a UC Berkeley computer scientist who heads DeepMind’s AI-safety-and-alignment department, wrote to us that she was partly motivated to take the job to acquire “the data, compute, and budget access to make progress on safety at the frontier.” Some professors who remain in academia are also forming partnerships with frontier labs or starting their own AI companies, in part so that they can pursue their research without resource constraints. And in some cases, would-be star Ph.D. students are dropping out of their graduate programs—or skipping them altogether—to pursue careers in AI instead.
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Although plenty of research is still happening within universities, many professors told us, the result is a flywheel: As more academics get sucked up by industry, the center of AI research moves further from academia, thus increasing the incentive for remaining researchers to leave. The exodus could have some advantages. Many of the professors currently at the labs are on temporary leave; some will likely return to academia full-time. When they do so, they’ll bring new knowledge about frontier research to their institutions. (Dragan is currently preparing to teach a small Ph.D. seminar on AI safety.) Scientists at AI labs might also innovate at a faster pace. After all, they don’t have to worry about applying for grants or waiting years for an article to go through peer review. “If some of the best research is being done in these companies, then I kind of want colleagues and faculty members to be there,” Chris Gregg, a Stanford computer scientist, told us. “You go where the best research is being done, and if that happens to be at a company, so be it.”
But when academics decamp to AI labs or spend more of their time working on their own companies, universities are left with fewer professors to teach the next generation of researchers. (Gregg said that students occasionally ask why courses are no longer offered. The answer is sometimes that the professor is on leave at an AI company.) When star professors leave universities, it becomes harder for students to do work that helps them stand out to prospective employers, Shi said. To differentiate themselves from other applicants, it helps to have worked with mentors who are doing consequential or cutting-edge research. Jennifer Chayes, the dean of the College of Computing, Data Science, and Society at UC Berkeley, told us she fears that as research concentrates in labs with proprietary models, it will be harder for people outside the labs to use AI models to advance science. “Computer-science departments at universities will survive this,” she said. “I don’t know if our innovation economy will.”
As the AI race intensifies, companies now decline to release much of their research for fear of giving away their competitive advantage. Both Gregg and Chayes said they hear from researchers who have gone to AI labs that they can’t publish the work they want to. “The top few AI companies share some things that are at the cutting edge, but it’s a very narrow slice of the potential AI literature that could exist,” Nathan Lambert, an independent AI researcher, told us. According to one study, when AI experts permanently transition from universities to companies, they publish roughly 65 percent fewer papers per year. Papers that do get published tend to emphasize progress on safety efforts and not frontier AI capabilities; corporate research can help fuel marketing hype. And with competition between AI companies escalating, it’s not only the research that is getting locked down, but AI models too. Last month, when Anthropic released a new powerful model called Fable, the company announced that it would invisibly degrade the model’s ability to do certain AI research. The move was positioned as a safety decision, but the resulting outcry from academia was strong. (Anthropic apologized and changed the policy.)
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Academics inside of the tech companies are more sanguine. Shi, the Georgia Tech professor who works at Nvidia, said the company is committed to open research, which is partly why he took the job. Meanwhile, Dragan, the Berkeley computer scientist at DeepMind, explained that publishing isn’t everything. “I find a much bigger priority than publishing is engaging in policy and regulation,” she wrote to us. We also reached out to spokespeople at OpenAI, Meta, and Anthropic for comment. Anthropic declined to comment; OpenAI and Meta did not respond.
The implications are potentially profound, not just for AI research, but for science more generally. In Silicon Valley, there’s a popular view that leading AI labs will end up as the primary drivers of science. “AGI has the potential to be the ultimate tool for advancing science and medicine,” DeepMind CEO Demis Hassabis wrote last week. Hassabis has previously said that an inspiration for his company is Bell Labs, the R&D arm of AT&T, which employed some 1,200 Ph.D.s at its height in the 1960s. Scientists at the lab earned at least 10 Nobel Prizes, and invented the transistor and the modern solar cell. Already, DeepMind has done Nobel Prize–winning research on protein-structure prediction, and the top models have already demonstrated impressive capabilities in mathematics research. On Sunday, a mathematician who works at Anthropic posted that he had used the company’s Fable model during the World Cup final to resolve a nearly 90-year-old mathematical problem.
But if scientific talent and computing power becomes concentrated inside private firms, Silicon Valley could also end up as a gatekeeper of science. Some are raising concerns: A group of academics recently published a declaration warning about “the increasing involvement of technology companies in mathematical research.” If left unchecked, they argued, the incursion of tech companies into research could affect “the scope and depth of mathematical research itself.” Instead of accelerating science, as the leaders of AI companies claim they will, they might end up suffocating it.
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